The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome
Open accessAbstract
Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures-unrelated to cancer-of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R(2) = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.
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1 retracted
RETRACTED: A Pleiotropically Acting MicroRNA, miR-31, Inhibits Breast Cancer Metastasis
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Luminal B Breast Cancer: Molecular Characterization, Clinical Management, and Future Perspectives
Ades, F., Zardavas, D., Bozovic-Spasojevic, I., Pugliano, L., Fumagalli, D., de Azambuja, E., Viale, G., Sotiriou, C., & Piccart, M. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.54.1870 · Cited by 435
Differential Glutamate Metabolism in Proliferating and Quiescent Mammary Epithelial Cells
Coloff, J. L., Murphy, J. P., Braun, C. R., Harris, I. S., Shelton, L. M., Kami, K., Gygi, S. P., Selfors, L. M., & Brugge, J. S. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.03.016 · Cited by 288
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Metadata
- Authors
- Venet, D., Dumont, J. E., & Detours, V.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-10-20
- Volume
- 7
- Issue
- 10
- Pages
- e1002240
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2022397091
- Cited by
- 586
- References
- 68
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
